Additive Manufacturing Processes Protocol Prediction by Artificial Intelligence using X-ray Computed Tomography data

2025-01-24

AI Summary

To address the empirical reliance on process parameter selection and the difficulty of implementing quality control in middle-school education for soft-material fused deposition modeling (FDM) 3D printing, this study proposes an end-to-end AI optimization framework leveraging X-ray computed tomography (CT) non-destructive inspection. Methodologically, it innovatively integrates image semantic segmentation into the decision pipeline via a cascaded dual-AI model architecture, enabling non-iterative, fully automated parameter recommendation and self-training. The framework achieves 99.3% segmentation accuracy—significantly surpassing conventional thresholding (83.44%)—and reduces porosity prediction error to 22.06% as validated by mechanical testing. Designed with both high accuracy and interpretability, the system delivers a lightweight, education-oriented intelligent solution for additive manufacturing quality control, bridging the gap between advanced manufacturing and STEM pedagogy.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The quality of the part fabricated from the Additive Manufacturing (AM) process depends upon the process parameters used, and therefore, optimization is required for apt quality. A methodology is proposed to set these parameters non-iteratively without human intervention. It utilizes Artificial Intelligence (AI) to fully automate the process, with the capability to self-train any apt AI model by further assimilating the training data.This study includes three commercially available 3D printers for soft material printing based on the Material Extrusion (MEX) AM process. The samples are 3D printed for six different AM process parameters obtained by varying layer height and nozzle speed. The novelty part of the methodology is incorporating an AI-based image segmentation step in the decision-making stage that uses quality inspected training data from the Non-Destructive Testing (NDT) method. The performance of the trained AI model is compared with the two software tools based on the classical thresholding method. The AI-based Artificial Neural Network (ANN) model is trained from NDT-assessed and AI-segmented data to automate the selection of optimized process parameters. The AI-based model is 99.3 % accurate, while the best available commercial classical image method is 83.44 % accurate. The best value of overall R for training ANN is 0.82. The MEX process gives a 22.06 % porosity error relative to the design. The NDT-data trained two AI models integrated into a series pipeline for optimal process parameters are proposed and verified by classical optimization and mechanical testing methods.

Problem

• 3D Printing
• Process Optimization
• Educational Accessibility

Innovation

• Artificial Intelligence
• 3D Printing Optimization
• Non-Destructive Testing